{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Box Time Series Cookbook\n",
    "\n",
    "This cookbook shows how to make a simple box time series for data for doing simple tests of spectroscopic data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tshirt.pipeline import phot_pipeline\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Edit the Extraction Parameters\n",
    "\n",
    "Here, we start with the default parameters and edit them. Alternatively, you can create a `yaml` file that contains the parameters. There is a database of example parameters here:\n",
    "https://github.com/eas342/tshirt/tree/master/tshirt/parameters/spec_params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Note: File Search comes up empty\n"
     ]
    }
   ],
   "source": [
    "default_phot = phot_pipeline.phot(directParam={})\n",
    "newParam = default_phot.param"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Tell tshirt where to find the data files\n",
    "newParam['procFiles'] = '../../../../Documents/hst/corot1/visit01/ibh*.fits'\n",
    "## Give your source a name\n",
    "newParam['srcName'] = 'CoRoT-1'\n",
    "newParam['srcNameShort'] = 'corot1'\n",
    "## Assign a name for the observing \"Night\" or name\n",
    "newParam['nightName'] = 'visit01'\n",
    "## Identify the box's spatial position. List of [x,y] lists\n",
    "newParam['refStarPos'] = [[60,73]]\n",
    "## Define the Geometry and size of the Aperture\n",
    "newParam['srcGeometry'] = 'Rectangular'\n",
    "newParam['apHeight'] = 10\n",
    "newParam['apWidth'] = 30\n",
    "## Define the background Geometry and size of Aperture\n",
    "newParam['backWidth'] = 30\n",
    "newParam['backHeight'] = 10\n",
    "newParam['bkgGeometry'] = 'Rectangular'\n",
    "## Choose an offset for the background box\n",
    "newParam['backOffset'] = [0.,15.] ## here it is offset in X by 0.0 px and Y by 15.0 px\n",
    "\n",
    "## Choose the FITS extension for the data\n",
    "newParam['FITSextension'] = 'SCI'\n",
    "\n",
    "## Set an offset epoch for time series (optional)\n",
    "newParam['jdRef'] = 2455951\n",
    "\n",
    "## Set the read noise\n",
    "newParam['readNoise'] = 20."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "phot = phot_pipeline.phot(directParam=newParam)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Check the aperture\n",
    "`showStarChoices` shows the source and background apertures we created.\n",
    "The label \"src\" is for the first star in the list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: AstropyDeprecationWarning: \"ax\" was deprecated in version 0.7 and will be removed in a future version. Use argument \"axes\" instead. [tshirt.pipeline.phot_pipeline]\n",
      "WARNING: AstropyDeprecationWarning: \"ax\" was deprecated in version 0.7 and will be removed in a future version. Use argument \"axes\" instead. [tshirt.pipeline.phot_pipeline]\n",
      "/Users/everettschlawin/anaconda/envs/py36/lib/python3.6/site-packages/tshirt/pipeline/phot_pipeline.py:333: UserWarning: Matplotlib is currently using module://ipykernel.pylab.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
      "  fig.show()\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "phot.showStarChoices(showAps=True,showPlot=True,apColor='red',backColor='green')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Do the Extraction on all images\n",
    "the `useMultiprocessing` keyword uses several simultaneous processors to speed things up"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "On 0 of 98\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: The \"exact\" method is not yet implemented for rectangular apertures -- using \"subpixel\" method with \"subpixels=32\" [photutils.aperture.core]\n",
      "WARNING: The \"exact\" method is not yet implemented for rectangular apertures -- using \"subpixel\" method with \"subpixels=32\" [photutils.aperture.core]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "On 15 of 98\n",
      "On 30 of 98\n",
      "On 45 of 98\n",
      "On 60 of 98\n",
      "On 75 of 98\n",
      "On 90 of 98\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: VerifyWarning: Card is too long, comment will be truncated. [astropy.io.fits.card]\n"
     ]
    }
   ],
   "source": [
    "phot.do_phot(useMultiprocessing=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from importlib import reload\n",
    "reload(phot_pipeline)\n",
    "phot = phot_pipeline.phot(directParam=newParam)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Retrieve the photometry"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "t1, t2 = phot.get_tSeries()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1035d8ef0>]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(t1['Time (JD)'],t1['Flux 0'],'o')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, the transit of CoRoT-1 b is clearly visible in the box photometry"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}